chapter 1
Harrison Linder
2024-01-23
below are the necessary libraries to use for this course
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.4.4 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.0
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(tidycensus)
library(sf)## Linking to GEOS 3.11.0, GDAL 3.5.3, PROJ 9.1.0; sf_use_s2() is TRUElibrary(kableExtra)##
## Attaching package: 'kableExtra'
##
## The following object is masked from 'package:dplyr':
##
## group_rowsoptions(scipen=999)
options(tigris_class = "sf")
# the below line was changed from the code provided in the book. the operator giver was "=", however that appeared to not save "root.dir" to the environment, so I chaged the operator to "<-"
root.dir <- "https://raw.githubusercontent.com/urbanSpatial/Public-Policy-Analytics-Landing/master/DATA/"
source("https://raw.githubusercontent.com/urbanSpatial/Public-Policy-Analytics-Landing/master/functions.r")Here is the color palette we are going to use for maps
palette5 <- c("#f0f9e8","#bae4bc","#7bccc4","#43a2ca","#0868ac")how to install my census api key
census_api_key("c08e727b3d32b823955aea424744c9dda3b30df0", install = TRUE, overwrite = TRUE)## Your original .Renviron will be backed up and stored in your R HOME directory if needed.## Your API key has been stored in your .Renviron and can be accessed by Sys.getenv("CENSUS_API_KEY").
## To use now, restart R or run `readRenviron("~/.Renviron")`## [1] "c08e727b3d32b823955aea424744c9dda3b30df0"# readRenviron("~/.Renviron") is for some reason needed for the api to be active in the current session
readRenviron("~/.Renviron")we will be using the following data
from the textbook: In this chapter, the tidycensus is used to query and return Census data directly into R. At the time of publication however, the Census has disabled the year 2000 endpoint/data.10 Instead, the code below downloads an equivalent data frame called tracts00. A data frame is the most common way to store data in R. The code block at the end of this section demonstrates a tidycensus call to return 2017 data.
tracts00 <-
st_read(file.path(root.dir,"/Chapter1/PHL_CT00.geojson")) %>%
st_transform('ESRI:102728')## Reading layer `PHL_CT00' from data source
## `https://raw.githubusercontent.com/urbanSpatial/Public-Policy-Analytics-Landing/master/DATA///Chapter1/PHL_CT00.geojson'
## using driver `GeoJSON'
## Simple feature collection with 2667 features and 4 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -75.28023 ymin: 39.86591 xmax: -74.95551 ymax: 40.13771
## Geodetic CRS: WGS 84tracts00[1:3,]## Simple feature collection with 3 features and 4 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: 2696968 ymin: -68663.64 xmax: 2700290 ymax: -65640.25
## Projected CRS: NAD_1983_StatePlane_Pennsylvania_North_FIPS_3701_Feet
## GEOID NAME variable value geometry
## 1 42101000100 Census Tract 1 P001001 2576 MULTIPOLYGON (((2697812 -65...
## 2 42101000100 Census Tract 1 P006002 2095 MULTIPOLYGON (((2697812 -65...
## 3 42101000100 Census Tract 1 PCT025050 48 MULTIPOLYGON (((2697812 -65...to find out how many census plots we have in our geography (Philadelphia), we can create a table the displays how many rows we have by variable. our data is long formatted so there are multiple rows representing a given census plot.
table(tracts00$variable)##
## H056001 P001001 P006002 P053001 P092001 PCT025009 PCT025050
## 381 381 381 381 381 381 381now we are going to plot population by census plot in the year 2000. To do that, we are going to create a variable the has filtered for only rows where the variable is that representing population in the year 2000.
totalpop00 <-
tracts00 %>%
filter(variable == "P001001")
#lets make sure we have the correct number of rows (381)
nrow(totalpop00) ## [1] 381# lets see a snapshot of our data
head(totalpop00)## Simple feature collection with 6 features and 4 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: 2688965 ymin: -68894.43 xmax: 2700290 ymax: -64443.03
## Projected CRS: NAD_1983_StatePlane_Pennsylvania_North_FIPS_3701_Feet
## GEOID NAME variable value geometry
## 1 42101000100 Census Tract 1 P001001 2576 MULTIPOLYGON (((2697812 -65...
## 2 42101000200 Census Tract 2 P001001 1355 MULTIPOLYGON (((2696038 -65...
## 3 42101000300 Census Tract 3 P001001 2577 MULTIPOLYGON (((2690929 -64...
## 4 42101000400 Census Tract 4 P001001 4387 MULTIPOLYGON (((2693009 -66...
## 5 42101000500 Census Tract 5 P001001 1071 MULTIPOLYGON (((2697206 -67...
## 6 42101000600 Census Tract 6 P001001 1384 MULTIPOLYGON (((2696968 -68...lets take a look at what the basic “plot function does for an {sf} layer
plot(totalpop00)
the textbook illustrates how to make progressively more useful maps using the ggplot function with geom_sf layers
from the textbook: A maps totalPop00, using the aes or aesthetic parameter to fill the tract polygons by the value field.
Plot B converts value to 5 quintile categories using the q5 function. These 5 categories are of class factor. Try q5(totalPop00$value).
Plot C adds fill color and legend improvements using the scale_fill_manual function. Many different scale types are possible.13 values is set to a list of colors, palette5. labels is set to the values of the associated quintiles (try qBr(totalPop00, “value”)). Finally, a legend name is added. inserts a hard return.
Plot D inserts a title using the labs parameter, as well as a mapTheme
Plot_A <-
ggplot() +
geom_sf(data = totalpop00, aes(fill = value))
Plot_B <-
ggplot() +
geom_sf(data = totalpop00, aes(fill = q5(value)))
Plot_C <-
ggplot() +
geom_sf(data = totalpop00, aes(fill = q5(value))) +
scale_fill_manual(values = palette5,
labels = qBr(totalpop00, "value"),
name = "Total\nPopluation\n(Quintile Breaks)")
Plot_D <-
ggplot() +
geom_sf(data = totalpop00, aes(fill = q5(value))) +
scale_fill_manual(values = palette5,
labels = qBr(totalpop00, "value"),
name = "Popluation\n(Quintile Breaks)") +
labs(title = "Total Population", subtitle = "Philadelphia; 2000") +
mapTheme()## Warning: The `size` argument of `element_rect()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.the above code saves our plots to their assigned variables. below we can print them.
print(Plot_A)
print(Plot_B)
print(Plot_C)
print(Plot_D)
next, the textbook converts the data from long format to wide format and
in general makes it easier to read.
tracts00 <-
dplyr::select(tracts00, -NAME) %>%
spread(variable, value) %>%
dplyr::select(-geometry) %>%
rename(TotalPop = P001001, Whites = P006002, MaleBachelors = PCT025009,
FemaleBachelors = PCT025050, MedHHInc = P053001, MedRent = H056001,
TotalPoverty = P092001)
st_drop_geometry(tracts00)[1:3,]## GEOID MedRent TotalPop Whites MedHHInc TotalPoverty MaleBachelors
## 1 42101000100 858 2576 2095 48886 1739 64
## 2 42101000200 339 1355 176 8349 505 23
## 3 42101000300 660 2577 1893 40625 1189 41
## FemaleBachelors
## 1 48
## 2 73
## 3 103lets change our data to wide format
tracts00 <-
tracts00 %>%
mutate(pctWhite = ifelse(TotalPop > 0, Whites / TotalPop, 0),
pctBachelors = ifelse(TotalPop > 0, ((FemaleBachelors + MaleBachelors) / TotalPop), 0),
pctPoverty = ifelse(TotalPop > 0, TotalPoverty / TotalPop, 0),
year = "2000") %>%
dplyr::select(-Whites,-FemaleBachelors,-MaleBachelors,-TotalPoverty)now we are going to query 2017 acs data to match our 2000 data. I am going to change the code from the textbook so that we can see the data change which each line of code.
tracts17 <-
get_acs(geography = "tract", variables = c("B25026_001E","B02001_002E","B15001_050E",
"B15001_009E","B19013_001E","B25058_001E",
"B06012_002E"),
year=2017, state=42, county=101, geometry=T, output="wide") %>%
#the above code specifies the query from the census API, note that more than just the specified variables are generated as columns. I'm not sure what the additional variables represent.
print() %>%
st_transform('ESRI:102728') %>%
# changes the CRS to what we have been using
print() %>%
rename(TotalPop = B25026_001E, Whites = B02001_002E,
FemaleBachelors = B15001_050E, MaleBachelors = B15001_009E,
MedHHInc = B19013_001E, MedRent = B25058_001E,
TotalPoverty = B06012_002E) %>%
#names our columns
print() %>%
dplyr::select(-NAME, -starts_with("B")) %>%
#removes names and all the columns that start with "b". all the variable codes start with b, so any columns that we didnt name get deleted
print() %>%
mutate(pctWhite = ifelse(TotalPop > 0, Whites / TotalPop,0),
pctBachelors = ifelse(TotalPop > 0, ((FemaleBachelors + MaleBachelors) / TotalPop),0),
pctPoverty = ifelse(TotalPop > 0, TotalPoverty / TotalPop, 0),
year = "2017") %>%
#calculates our statistics of interest as a rate instead of a count. we use ifelse functions to account for cases in which we have 0 numerator or denominator. that could be a result of incomplete data.
print() %>%
dplyr::select(-Whites, -FemaleBachelors, -MaleBachelors, -TotalPoverty) ## Getting data from the 2013-2017 5-year ACS## Downloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.##
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## Simple feature collection with 384 features and 16 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -75.28027 ymin: 39.86705 xmax: -74.95578 ymax: 40.13799
## Geodetic CRS: NAD83
## First 10 features:
## GEOID NAME
## 1 42101000200 Census Tract 2, Philadelphia County, Pennsylvania
## 2 42101000500 Census Tract 5, Philadelphia County, Pennsylvania
## 3 42101000901 Census Tract 9.01, Philadelphia County, Pennsylvania
## 4 42101001002 Census Tract 10.02, Philadelphia County, Pennsylvania
## 5 42101002000 Census Tract 20, Philadelphia County, Pennsylvania
## 6 42101003901 Census Tract 39.01, Philadelphia County, Pennsylvania
## 7 42101007102 Census Tract 71.02, Philadelphia County, Pennsylvania
## 8 42101008701 Census Tract 87.01, Philadelphia County, Pennsylvania
## 9 42101010200 Census Tract 102, Philadelphia County, Pennsylvania
## 10 42101011500 Census Tract 115, Philadelphia County, Pennsylvania
## B25026_001E B25026_001M B02001_002E B02001_002M B15001_050E B15001_050M
## 1 2479 424 698 196 145 90
## 2 1375 124 1096 292 61 27
## 3 1949 179 1452 209 96 49
## 4 3259 264 3056 251 26 23
## 5 2154 222 534 133 6 7
## 6 6583 810 4597 840 124 91
## 7 4944 740 174 127 7 10
## 8 4356 499 2194 358 206 101
## 9 3311 481 0 10 0 10
## 10 4686 431 189 95 0 10
## B15001_009E B15001_009M B19013_001E B19013_001M B25058_001E B25058_001M
## 1 113 62 30746 8706 1051 128
## 2 55 45 42135 12885 1363 174
## 3 39 30 42548 9262 1095 62
## 4 15 14 109020 12211 1598 103
## 5 39 40 41335 6339 841 47
## 6 40 47 38301 3245 774 49
## 7 0 10 31332 6631 708 101
## 8 251 134 44225 4124 908 52
## 9 0 10 18989 5279 486 79
## 10 18 28 51155 4231 788 28
## B06012_002E B06012_002M geometry
## 1 962 378 MULTIPOLYGON (((-75.16234 3...
## 2 536 107 MULTIPOLYGON (((-75.16506 3...
## 3 473 131 MULTIPOLYGON (((-75.16484 3...
## 4 211 163 MULTIPOLYGON (((-75.15034 3...
## 5 365 139 MULTIPOLYGON (((-75.18824 3...
## 6 1423 504 MULTIPOLYGON (((-75.17691 3...
## 7 1715 610 MULTIPOLYGON (((-75.23363 3...
## 8 1094 320 MULTIPOLYGON (((-75.21272 3...
## 9 1337 469 MULTIPOLYGON (((-75.23536 3...
## 10 739 315 MULTIPOLYGON (((-75.25592 3...
## Simple feature collection with 384 features and 16 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: 2660774 ymin: -98728.57 xmax: 2750287 ymax: 1626.778
## Projected CRS: NAD_1983_StatePlane_Pennsylvania_North_FIPS_3701_Feet
## First 10 features:
## GEOID NAME
## 1 42101000200 Census Tract 2, Philadelphia County, Pennsylvania
## 2 42101000500 Census Tract 5, Philadelphia County, Pennsylvania
## 3 42101000901 Census Tract 9.01, Philadelphia County, Pennsylvania
## 4 42101001002 Census Tract 10.02, Philadelphia County, Pennsylvania
## 5 42101002000 Census Tract 20, Philadelphia County, Pennsylvania
## 6 42101003901 Census Tract 39.01, Philadelphia County, Pennsylvania
## 7 42101007102 Census Tract 71.02, Philadelphia County, Pennsylvania
## 8 42101008701 Census Tract 87.01, Philadelphia County, Pennsylvania
## 9 42101010200 Census Tract 102, Philadelphia County, Pennsylvania
## 10 42101011500 Census Tract 115, Philadelphia County, Pennsylvania
## B25026_001E B25026_001M B02001_002E B02001_002M B15001_050E B15001_050M
## 1 2479 424 698 196 145 90
## 2 1375 124 1096 292 61 27
## 3 1949 179 1452 209 96 49
## 4 3259 264 3056 251 26 23
## 5 2154 222 534 133 6 7
## 6 6583 810 4597 840 124 91
## 7 4944 740 174 127 7 10
## 8 4356 499 2194 358 206 101
## 9 3311 481 0 10 0 10
## 10 4686 431 189 95 0 10
## B15001_009E B15001_009M B19013_001E B19013_001M B25058_001E B25058_001M
## 1 113 62 30746 8706 1051 128
## 2 55 45 42135 12885 1363 174
## 3 39 30 42548 9262 1095 62
## 4 15 14 109020 12211 1598 103
## 5 39 40 41335 6339 841 47
## 6 40 47 38301 3245 774 49
## 7 0 10 31332 6631 708 101
## 8 251 134 44225 4124 908 52
## 9 0 10 18989 5279 486 79
## 10 18 28 51155 4231 788 28
## B06012_002E B06012_002M geometry
## 1 962 378 MULTIPOLYGON (((2694006 -65...
## 2 536 107 MULTIPOLYGON (((2693289 -66...
## 3 473 131 MULTIPOLYGON (((2693422 -69...
## 4 211 163 MULTIPOLYGON (((2697524 -70...
## 5 365 139 MULTIPOLYGON (((2686940 -72...
## 6 1423 504 MULTIPOLYGON (((2690317 -78...
## 7 1715 610 MULTIPOLYGON (((2674209 -72...
## 8 1094 320 MULTIPOLYGON (((2679966 -68...
## 9 1337 469 MULTIPOLYGON (((2673427 -61...
## 10 739 315 MULTIPOLYGON (((2667564 -58...
## Simple feature collection with 384 features and 16 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: 2660774 ymin: -98728.57 xmax: 2750287 ymax: 1626.778
## Projected CRS: NAD_1983_StatePlane_Pennsylvania_North_FIPS_3701_Feet
## First 10 features:
## GEOID NAME TotalPop
## 1 42101000200 Census Tract 2, Philadelphia County, Pennsylvania 2479
## 2 42101000500 Census Tract 5, Philadelphia County, Pennsylvania 1375
## 3 42101000901 Census Tract 9.01, Philadelphia County, Pennsylvania 1949
## 4 42101001002 Census Tract 10.02, Philadelphia County, Pennsylvania 3259
## 5 42101002000 Census Tract 20, Philadelphia County, Pennsylvania 2154
## 6 42101003901 Census Tract 39.01, Philadelphia County, Pennsylvania 6583
## 7 42101007102 Census Tract 71.02, Philadelphia County, Pennsylvania 4944
## 8 42101008701 Census Tract 87.01, Philadelphia County, Pennsylvania 4356
## 9 42101010200 Census Tract 102, Philadelphia County, Pennsylvania 3311
## 10 42101011500 Census Tract 115, Philadelphia County, Pennsylvania 4686
## B25026_001M Whites B02001_002M FemaleBachelors B15001_050M MaleBachelors
## 1 424 698 196 145 90 113
## 2 124 1096 292 61 27 55
## 3 179 1452 209 96 49 39
## 4 264 3056 251 26 23 15
## 5 222 534 133 6 7 39
## 6 810 4597 840 124 91 40
## 7 740 174 127 7 10 0
## 8 499 2194 358 206 101 251
## 9 481 0 10 0 10 0
## 10 431 189 95 0 10 18
## B15001_009M MedHHInc B19013_001M MedRent B25058_001M TotalPoverty
## 1 62 30746 8706 1051 128 962
## 2 45 42135 12885 1363 174 536
## 3 30 42548 9262 1095 62 473
## 4 14 109020 12211 1598 103 211
## 5 40 41335 6339 841 47 365
## 6 47 38301 3245 774 49 1423
## 7 10 31332 6631 708 101 1715
## 8 134 44225 4124 908 52 1094
## 9 10 18989 5279 486 79 1337
## 10 28 51155 4231 788 28 739
## B06012_002M geometry
## 1 378 MULTIPOLYGON (((2694006 -65...
## 2 107 MULTIPOLYGON (((2693289 -66...
## 3 131 MULTIPOLYGON (((2693422 -69...
## 4 163 MULTIPOLYGON (((2697524 -70...
## 5 139 MULTIPOLYGON (((2686940 -72...
## 6 504 MULTIPOLYGON (((2690317 -78...
## 7 610 MULTIPOLYGON (((2674209 -72...
## 8 320 MULTIPOLYGON (((2679966 -68...
## 9 469 MULTIPOLYGON (((2673427 -61...
## 10 315 MULTIPOLYGON (((2667564 -58...
## Simple feature collection with 384 features and 8 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: 2660774 ymin: -98728.57 xmax: 2750287 ymax: 1626.778
## Projected CRS: NAD_1983_StatePlane_Pennsylvania_North_FIPS_3701_Feet
## First 10 features:
## GEOID TotalPop Whites FemaleBachelors MaleBachelors MedHHInc MedRent
## 1 42101000200 2479 698 145 113 30746 1051
## 2 42101000500 1375 1096 61 55 42135 1363
## 3 42101000901 1949 1452 96 39 42548 1095
## 4 42101001002 3259 3056 26 15 109020 1598
## 5 42101002000 2154 534 6 39 41335 841
## 6 42101003901 6583 4597 124 40 38301 774
## 7 42101007102 4944 174 7 0 31332 708
## 8 42101008701 4356 2194 206 251 44225 908
## 9 42101010200 3311 0 0 0 18989 486
## 10 42101011500 4686 189 0 18 51155 788
## TotalPoverty geometry
## 1 962 MULTIPOLYGON (((2694006 -65...
## 2 536 MULTIPOLYGON (((2693289 -66...
## 3 473 MULTIPOLYGON (((2693422 -69...
## 4 211 MULTIPOLYGON (((2697524 -70...
## 5 365 MULTIPOLYGON (((2686940 -72...
## 6 1423 MULTIPOLYGON (((2690317 -78...
## 7 1715 MULTIPOLYGON (((2674209 -72...
## 8 1094 MULTIPOLYGON (((2679966 -68...
## 9 1337 MULTIPOLYGON (((2673427 -61...
## 10 739 MULTIPOLYGON (((2667564 -58...
## Simple feature collection with 384 features and 12 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: 2660774 ymin: -98728.57 xmax: 2750287 ymax: 1626.778
## Projected CRS: NAD_1983_StatePlane_Pennsylvania_North_FIPS_3701_Feet
## First 10 features:
## GEOID TotalPop Whites FemaleBachelors MaleBachelors MedHHInc MedRent
## 1 42101000200 2479 698 145 113 30746 1051
## 2 42101000500 1375 1096 61 55 42135 1363
## 3 42101000901 1949 1452 96 39 42548 1095
## 4 42101001002 3259 3056 26 15 109020 1598
## 5 42101002000 2154 534 6 39 41335 841
## 6 42101003901 6583 4597 124 40 38301 774
## 7 42101007102 4944 174 7 0 31332 708
## 8 42101008701 4356 2194 206 251 44225 908
## 9 42101010200 3311 0 0 0 18989 486
## 10 42101011500 4686 189 0 18 51155 788
## TotalPoverty geometry pctWhite pctBachelors
## 1 962 MULTIPOLYGON (((2694006 -65... 0.28156515 0.104074223
## 2 536 MULTIPOLYGON (((2693289 -66... 0.79709091 0.084363636
## 3 473 MULTIPOLYGON (((2693422 -69... 0.74499743 0.069266290
## 4 211 MULTIPOLYGON (((2697524 -70... 0.93771095 0.012580546
## 5 365 MULTIPOLYGON (((2686940 -72... 0.24791086 0.020891365
## 6 1423 MULTIPOLYGON (((2690317 -78... 0.69831384 0.024912654
## 7 1715 MULTIPOLYGON (((2674209 -72... 0.03519417 0.001415858
## 8 1094 MULTIPOLYGON (((2679966 -68... 0.50367309 0.104912764
## 9 1337 MULTIPOLYGON (((2673427 -61... 0.00000000 0.000000000
## 10 739 MULTIPOLYGON (((2667564 -58... 0.04033291 0.003841229
## pctPoverty year
## 1 0.38805970 2017
## 2 0.38981818 2017
## 3 0.24268856 2017
## 4 0.06474379 2017
## 5 0.16945218 2017
## 6 0.21616284 2017
## 7 0.34688511 2017
## 8 0.25114784 2017
## 9 0.40380550 2017
## 10 0.15770380 2017 #removes the selected columns. the - operator specifies that we want everything besides the specified columns
head(tracts17)## Simple feature collection with 6 features and 8 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: 2686791 ymin: -79202.01 xmax: 2699682 ymax: -65257.88
## Projected CRS: NAD_1983_StatePlane_Pennsylvania_North_FIPS_3701_Feet
## GEOID TotalPop MedHHInc MedRent pctWhite pctBachelors pctPoverty year
## 1 42101000200 2479 30746 1051 0.2815651 0.10407422 0.38805970 2017
## 2 42101000500 1375 42135 1363 0.7970909 0.08436364 0.38981818 2017
## 3 42101000901 1949 42548 1095 0.7449974 0.06926629 0.24268856 2017
## 4 42101001002 3259 109020 1598 0.9377110 0.01258055 0.06474379 2017
## 5 42101002000 2154 41335 841 0.2479109 0.02089136 0.16945218 2017
## 6 42101003901 6583 38301 774 0.6983138 0.02491265 0.21616284 2017
## geometry
## 1 MULTIPOLYGON (((2694006 -65...
## 2 MULTIPOLYGON (((2693289 -66...
## 3 MULTIPOLYGON (((2693422 -69...
## 4 MULTIPOLYGON (((2697524 -70...
## 5 MULTIPOLYGON (((2686940 -72...
## 6 MULTIPOLYGON (((2690317 -78...now we are going to combine our two data sets, one from 2000, one from 2017, together
allTracts <- rbind(tracts00,tracts17)philly transit open data
from the textbook: The code below downloads and binds together El and Broad St. station locations into a single layer, septaStops. st_read downloads the data in geojson form (with geometries) from the web. A Line field is generated and selected along with the Station field. Lastly, the data is projected into the same coordinate system as tracts00.
septaStops <-
rbind(
st_read("https://opendata.arcgis.com/datasets/8c6e2575c8ad46eb887e6bb35825e1a6_0.geojson") %>%
mutate(Line = "El") %>% #this line adds the label 'El' to a newly created "Line" Column
select(Station, Line),
st_read("https://opendata.arcgis.com/datasets/2e9037fd5bef406488ffe5bb67d21312_0.geojson") %>%
mutate(Line ="Broad_St") %>%
select(Station, Line)) %>%
st_transform(st_crs(tracts00)) #note how you can call the crs from the tracts00 dataframe using str_crs## Reading layer `SEPTA_-_Market-Frankford_Line_Stations' from data source
## `https://opendata.arcgis.com/datasets/8c6e2575c8ad46eb887e6bb35825e1a6_0.geojson'
## using driver `GeoJSON'
## Simple feature collection with 28 features and 45 fields
## Geometry type: POINT
## Dimension: XY
## Bounding box: xmin: -75.25973 ymin: 39.94989 xmax: -75.07788 ymax: 40.02299
## Geodetic CRS: WGS 84
## Reading layer `SEPTA_-_Broad_Street_Line_Stations' from data source
## `https://opendata.arcgis.com/datasets/2e9037fd5bef406488ffe5bb67d21312_0.geojson'
## using driver `GeoJSON'
## Simple feature collection with 24 features and 45 fields
## Geometry type: POINT
## Dimension: XY
## Bounding box: xmin: -75.17394 ymin: 39.90543 xmax: -75.13679 ymax: 40.04185
## Geodetic CRS: WGS 84lets map our stops onto the city
ggplot() + #creates the plot
geom_sf(data=st_union(tracts00)) + #adds the dissolved tract boundaries city map as a layer
geom_sf(data=septaStops, aes(colour = Line), show.legend = "point", size= 2) + # adds the septaStops as a layer to the map with different colors for each of the values in the Line column
scale_colour_manual(values = c("orange","blue")) +
labs(title="Septa Stops", subtitle="Philadelphia, PA") +
mapTheme()
now we are going to create “buffer” areas around each stop to be considered as transit oriented areas
septaBuffers <-
rbind(
st_buffer(septaStops, 2640) %>% # creates the buffers of 2640 feet (1/2 mile).
mutate(Legend = "Buffer") %>% # creates legend column with a buffer label for each stop buffer
dplyr::select(Legend), #selects only the legend column along with the geometry to remain in the dataframe
st_union(st_buffer(septaStops, 2640)) %>%
st_sf() %>% #this function is for some reason needed to convert the union transit areas created with the last line into an sf layer
mutate(Legend = "Unioned Buffer"))
ggplot() +
geom_sf(data=septaBuffers) +
geom_sf(data=septaStops, show.legend = "point") +
facet_wrap(~Legend) + #facet wrap can only be performed on long data
mapTheme()
now we need to select which tracts constitute part of the trasit oriented area. there are three methods that the book discusses for selecting tracts. The first is clipping, which selects the interesections of the tract and the buffer. This is problematic because the data for each tract aligns with the whole tract and its unclear how the clipping method divides the other variables if at all after subtracting area.
buffer <- filter(septaBuffers, Legend=="Unioned Buffer")
clip <-
st_intersection(buffer, tracts00) %>%
dplyr::select(TotalPop) %>%
mutate(Selection_Type = "Clip")## Warning: attribute variables are assumed to be spatially constant throughout
## all geometriesthe next method is to select any geometries that intersect with the buffer
selection <-
tracts00[buffer,] %>%
dplyr::select(TotalPop) %>%
mutate(Selection_Type = "Spatial Selection")and the last method is to choose all tracts of which’s “center of gravity” fall within the buffer. you imigane the ceter of gravity as at what point you would need to put your finger to balance the shape atop of it. This is the preffered method of the author.
selectCentroids <-
st_centroid(tracts00)[buffer,] %>% #creates centroid point geometry for tracts whose centroid is within the buffer
print() %>%
st_drop_geometry() %>% #this converts the sf into a dataframe to allow for it to be joined back with the original sf. I think that this is necessary because there cannot be more than one geometry per row in an sf
print() %>%
left_join(dplyr::select(tracts00, GEOID)) %>% #this joins the two data frames by the unique geoids. so it seems that since our centroid frame is the first variable, we get only matches that exist from that frame. and what we are matching it to is only the GEOID from tracts00. since tracts is a sf it seems like you are unable to select away the geometry
print() %>%
st_sf() %>% # converts dataframe back to sf
dplyr::select(TotalPop) %>% #for some reason we only want total population
mutate(Selection_Type = "Select by Centroids") #creates the select by centroids value in the Selection_type column. this will be useful for the side by side map we are going to create## Warning: st_centroid assumes attributes are constant over geometries## Simple feature collection with 97 features and 8 fields
## Geometry type: POINT
## Dimension: XY
## Bounding box: xmin: 2669191 ymin: -83405.65 xmax: 2716999 ymax: -34081.94
## Projected CRS: NAD_1983_StatePlane_Pennsylvania_North_FIPS_3701_Feet
## First 10 features:
## GEOID MedRent TotalPop MedHHInc pctWhite pctBachelors pctPoverty year
## 1 42101000100 858 2576 48886 0.8132764 0.043478261 0.6750776 2000
## 2 42101000200 339 1355 8349 0.1298893 0.070848708 0.3726937 2000
## 3 42101000300 660 2577 40625 0.7345751 0.055878929 0.4613892 2000
## 4 42101000400 601 4387 27400 0.7611124 0.028949168 0.6856622 2000
## 5 42101000500 501 1071 9620 0.3968254 0.006535948 0.2978525 2000
## 6 42101000600 913 1384 41563 0.7037572 0.067919075 0.4017341 2000
## 7 42101000700 844 2550 34536 0.8333333 0.075686275 0.7101961 2000
## 8 42101000800 904 8461 42346 0.8419809 0.066422409 0.7229642 2000
## 9 42101000900 575 4969 20725 0.7037633 0.146508352 0.6415778 2000
## 10 42101001000 873 5808 72625 0.9133953 0.011019284 0.6234504 2000
## geometry
## 1 POINT (2698593 -67134.68)
## 2 POINT (2695637 -66133.72)
## 3 POINT (2691672 -65624.79)
## 4 POINT (2691447 -66938.9)
## 5 POINT (2695339 -67444.62)
## 6 POINT (2695341 -68393.71)
## 7 POINT (2691290 -67859.72)
## 8 POINT (2691053 -68560.58)
## 9 POINT (2695146 -69101.59)
## 10 POINT (2698155 -69801.43)
## GEOID MedRent TotalPop MedHHInc pctWhite pctBachelors pctPoverty
## 1 42101000100 858 2576 48886 0.813276398 0.0434782609 0.67507764
## 2 42101000200 339 1355 8349 0.129889299 0.0708487085 0.37269373
## 3 42101000300 660 2577 40625 0.734575087 0.0558789290 0.46138921
## 4 42101000400 601 4387 27400 0.761112377 0.0289491680 0.68566218
## 5 42101000500 501 1071 9620 0.396825397 0.0065359477 0.29785247
## 6 42101000600 913 1384 41563 0.703757225 0.0679190751 0.40173410
## 7 42101000700 844 2550 34536 0.833333333 0.0756862745 0.71019608
## 8 42101000800 904 8461 42346 0.841980853 0.0664224087 0.72296419
## 9 42101000900 575 4969 20725 0.703763333 0.1465083518 0.64157778
## 10 42101001000 873 5808 72625 0.913395317 0.0110192837 0.62345041
## 11 42101001100 652 5925 36564 0.797468354 0.0494514768 0.65029536
## 14 42101001400 450 3528 26897 0.320011338 0.0229591837 0.52636054
## 15 42101001500 622 2192 38026 0.598540146 0.0369525547 0.53102190
## 18 42101001800 514 2462 36458 0.689277011 0.0170593014 0.51056052
## 19 42101001900 422 2689 21766 0.024172555 0.0081814801 0.38750465
## 22 42101002200 487 2135 30156 0.121779859 0.0051522248 0.41358314
## 23 42101002300 425 2644 29806 0.660363086 0.0230711044 0.43154312
## 24 42101002400 474 4262 34247 0.634209291 0.0229938996 0.47536368
## 28 42101002800 427 9255 22759 0.613182064 0.0143706105 0.39535386
## 29 42101002900 488 4050 26744 0.855308642 0.0214814815 0.48790123
## 30 42101003000 410 8160 20294 0.150000000 0.0019607843 0.34595588
## 40 42101003901 463 6192 26347 0.749677003 0.0050064599 0.43782300
## 41 42101003902 468 5630 36472 0.970692718 0.0031971581 0.43570160
## 42 42101004001 457 4036 25854 0.921704658 0.0101585728 0.45936571
## 43 42101004002 484 5024 31045 0.984076433 0.0151273885 0.43829618
## 50 42101004500 520 3049 36335 0.951459495 0.0062315513 0.44375205
## 52 42101004700 622 2540 44080 0.973622047 0.0031496063 0.41141732
## 53 42101004800 638 500 30455 1.000000000 0.0000000000 0.42000000
## 87 42101008301 472 4420 24800 0.020361991 0.0029411765 0.39321267
## 88 42101008302 425 4535 24567 0.005071665 0.0000000000 0.40044101
## 89 42101008400 414 4798 26341 0.015006253 0.0062526053 0.37265527
## 90 42101008500 381 6539 22077 0.028903502 0.0051995718 0.40266096
## 91 42101008600 443 6224 19612 0.154723650 0.0215295630 0.45983290
## 92 42101008700 507 6733 21131 0.457151344 0.0957968216 0.50378732
## 93 42101008800 590 8307 9320 0.584326472 0.1001564945 0.25954015
## 94 42101008900 538 3343 6311 0.561471732 0.1011067903 0.15465151
## 95 42101009000 571 5335 13792 0.621368322 0.0391752577 0.29090909
## 96 42101009100 350 2737 17500 0.299963464 0.0226525393 0.51589331
## 97 42101009200 307 3150 20083 0.026666667 0.0111111111 0.38634921
## 98 42101009300 377 4609 21503 0.002603602 0.0000000000 0.40203949
## 99 42101009400 285 3871 17743 0.009299923 0.0000000000 0.42934642
## 100 42101009500 368 3588 21058 0.000000000 0.0027870680 0.38099220
## 101 42101009600 404 4504 23171 0.017984014 0.0000000000 0.37655417
## 105 42101010000 421 4256 31415 0.336701128 0.0063439850 0.37946429
## 107 42101010200 331 3240 20634 0.008950617 0.0024691358 0.41172840
## 109 42101010400 374 3498 21772 0.010863350 0.0022870212 0.36678102
## 131 42101012600 916 801 31838 0.485642946 0.0399500624 0.40823970
## 132 42101012700 223 689 7500 0.076923077 0.0000000000 0.33671988
## 133 42101012800 841 231 39643 0.831168831 0.0303030303 0.64935065
## 134 42101012900 586 526 41000 0.922053232 0.0000000000 0.54372624
## 135 42101013000 573 1003 35625 0.417746760 0.0368893320 0.50149551
## 137 42101013200 177 2190 14375 0.051598174 0.0000000000 0.31735160
## 138 42101013300 492 2217 29352 0.274695535 0.0211998196 0.46098331
## 145 42101014000 370 2855 13458 0.042732049 0.0122591944 0.41015762
## 146 42101014100 210 2366 12165 0.094674556 0.0000000000 0.41758242
## 147 42101014200 565 2091 30862 0.608321377 0.0162601626 0.45911047
## 149 42101014400 323 3217 23720 0.393534349 0.0027976376 0.35685421
## 151 42101014600 384 3231 26295 0.194986072 0.0000000000 0.30238316
## 152 42101014700 306 2449 16411 0.008166599 0.0000000000 0.31073908
## 158 42101015300 353 3382 14826 0.068598462 0.0165582496 0.34920166
## 159 42101015400 489 2316 31923 0.410621762 0.0000000000 0.07340242
## 161 42101015600 326 1989 14524 0.227249874 0.0045248869 0.29562594
## 162 42101015700 339 2759 22478 0.355201160 0.0050743023 0.32584270
## 163 42101015800 387 5854 31862 0.953877690 0.0015374103 0.38503587
## 166 42101016100 349 5435 19098 0.605887764 0.0000000000 0.32750690
## 167 42101016200 337 2442 13833 0.223177723 0.0000000000 0.32186732
## 168 42101016300 341 3776 15865 0.181938559 0.0052966102 0.28257415
## 170 42101016500 330 2722 16964 0.011756062 0.0022042616 0.33357825
## 171 42101016600 353 1630 14250 0.025766871 0.0055214724 0.39693252
## 172 42101016700 363 6947 16367 0.002303152 0.0061897222 0.37339859
## 179 42101017300 427 3126 21731 0.008637236 0.0000000000 0.36852207
## 180 42101017400 339 2667 15050 0.024371954 0.0029996250 0.34833146
## 182 42101017601 325 5863 11909 0.240661777 0.0010233669 0.30291660
## 184 42101017700 370 9159 15687 0.313789715 0.0005459111 0.28976963
## 185 42101017800 389 6340 14793 0.502996845 0.0037854890 0.33280757
## 195 42101018800 385 7257 22489 0.686371779 0.0000000000 0.34463277
## 196 42101018900 412 942 24032 0.580679406 0.0084925690 0.33757962
## 197 42101019000 427 6869 24650 0.591789198 0.0000000000 0.32581162
## 199 42101019200 390 7519 19375 0.408032983 0.0014629605 0.30110387
## 204 42101019700 422 7139 19877 0.186300602 0.0016809077 0.29079703
## 207 42101020000 378 1756 22786 0.185649203 0.0182232346 0.30410023
## 208 42101020100 349 7732 20436 0.018882566 0.0010346611 0.40597517
## 210 42101020300 371 3147 23277 0.032729584 0.0019065777 0.34413727
## 211 42101020400 386 3398 29814 0.016774573 0.0020600353 0.34873455
## 283 42101027500 472 4823 33953 0.226622434 0.0008293593 0.32821895
## 284 42101027600 463 3932 31316 0.033825025 0.0073753815 0.39267548
## 286 42101027800 426 4867 25690 0.034929114 0.0000000000 0.41956030
## 288 42101028000 485 4819 28281 0.021996265 0.0076779415 0.35692052
## 289 42101028100 401 4051 36129 0.057516663 0.0108615157 0.32189583
## 290 42101028200 457 5448 29142 0.137114537 0.0014684288 0.30231278
## 291 42101028300 425 7270 24725 0.048693260 0.0011004127 0.33301238
## 301 42101029300 375 2898 19205 0.652173913 0.0027605245 0.34817115
## 302 42101029400 386 3232 21288 0.368811881 0.0021658416 0.34096535
## 308 42101030000 388 7150 23876 0.322517483 0.0036363636 0.35356643
## 309 42101030100 427 6050 32224 0.505289256 0.0019834711 0.36082645
## 310 42101030200 411 6694 39072 0.695100090 0.0043322378 0.36241410
## 381 42101036600 1505 618 87027 0.953074434 0.0161812298 0.61650485
## year
## 1 2000
## 2 2000
## 3 2000
## 4 2000
## 5 2000
## 6 2000
## 7 2000
## 8 2000
## 9 2000
## 10 2000
## 11 2000
## 14 2000
## 15 2000
## 18 2000
## 19 2000
## 22 2000
## 23 2000
## 24 2000
## 28 2000
## 29 2000
## 30 2000
## 40 2000
## 41 2000
## 42 2000
## 43 2000
## 50 2000
## 52 2000
## 53 2000
## 87 2000
## 88 2000
## 89 2000
## 90 2000
## 91 2000
## 92 2000
## 93 2000
## 94 2000
## 95 2000
## 96 2000
## 97 2000
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## 99 2000
## 100 2000
## 101 2000
## 105 2000
## 107 2000
## 109 2000
## 131 2000
## 132 2000
## 133 2000
## 134 2000
## 135 2000
## 137 2000
## 138 2000
## 145 2000
## 146 2000
## 147 2000
## 149 2000
## 151 2000
## 152 2000
## 158 2000
## 159 2000
## 161 2000
## 162 2000
## 163 2000
## 166 2000
## 167 2000
## 168 2000
## 170 2000
## 171 2000
## 172 2000
## 179 2000
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## 185 2000
## 195 2000
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## 199 2000
## 204 2000
## 207 2000
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## 210 2000
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## 283 2000
## 284 2000
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## 289 2000
## 290 2000
## 291 2000
## 301 2000
## 302 2000
## 308 2000
## 309 2000
## 310 2000
## 381 2000## Joining with `by = join_by(GEOID)`## GEOID MedRent TotalPop MedHHInc pctWhite pctBachelors pctPoverty
## 1 42101000100 858 2576 48886 0.813276398 0.0434782609 0.67507764
## 2 42101000200 339 1355 8349 0.129889299 0.0708487085 0.37269373
## 3 42101000300 660 2577 40625 0.734575087 0.0558789290 0.46138921
## 4 42101000400 601 4387 27400 0.761112377 0.0289491680 0.68566218
## 5 42101000500 501 1071 9620 0.396825397 0.0065359477 0.29785247
## 6 42101000600 913 1384 41563 0.703757225 0.0679190751 0.40173410
## 7 42101000700 844 2550 34536 0.833333333 0.0756862745 0.71019608
## 8 42101000800 904 8461 42346 0.841980853 0.0664224087 0.72296419
## 9 42101000900 575 4969 20725 0.703763333 0.1465083518 0.64157778
## 10 42101001000 873 5808 72625 0.913395317 0.0110192837 0.62345041
## 11 42101001100 652 5925 36564 0.797468354 0.0494514768 0.65029536
## 12 42101001400 450 3528 26897 0.320011338 0.0229591837 0.52636054
## 13 42101001500 622 2192 38026 0.598540146 0.0369525547 0.53102190
## 14 42101001800 514 2462 36458 0.689277011 0.0170593014 0.51056052
## 15 42101001900 422 2689 21766 0.024172555 0.0081814801 0.38750465
## 16 42101002200 487 2135 30156 0.121779859 0.0051522248 0.41358314
## 17 42101002300 425 2644 29806 0.660363086 0.0230711044 0.43154312
## 18 42101002400 474 4262 34247 0.634209291 0.0229938996 0.47536368
## 19 42101002800 427 9255 22759 0.613182064 0.0143706105 0.39535386
## 20 42101002900 488 4050 26744 0.855308642 0.0214814815 0.48790123
## 21 42101003000 410 8160 20294 0.150000000 0.0019607843 0.34595588
## 22 42101003901 463 6192 26347 0.749677003 0.0050064599 0.43782300
## 23 42101003902 468 5630 36472 0.970692718 0.0031971581 0.43570160
## 24 42101004001 457 4036 25854 0.921704658 0.0101585728 0.45936571
## 25 42101004002 484 5024 31045 0.984076433 0.0151273885 0.43829618
## 26 42101004500 520 3049 36335 0.951459495 0.0062315513 0.44375205
## 27 42101004700 622 2540 44080 0.973622047 0.0031496063 0.41141732
## 28 42101004800 638 500 30455 1.000000000 0.0000000000 0.42000000
## 29 42101008301 472 4420 24800 0.020361991 0.0029411765 0.39321267
## 30 42101008302 425 4535 24567 0.005071665 0.0000000000 0.40044101
## 31 42101008400 414 4798 26341 0.015006253 0.0062526053 0.37265527
## 32 42101008500 381 6539 22077 0.028903502 0.0051995718 0.40266096
## 33 42101008600 443 6224 19612 0.154723650 0.0215295630 0.45983290
## 34 42101008700 507 6733 21131 0.457151344 0.0957968216 0.50378732
## 35 42101008800 590 8307 9320 0.584326472 0.1001564945 0.25954015
## 36 42101008900 538 3343 6311 0.561471732 0.1011067903 0.15465151
## 37 42101009000 571 5335 13792 0.621368322 0.0391752577 0.29090909
## 38 42101009100 350 2737 17500 0.299963464 0.0226525393 0.51589331
## 39 42101009200 307 3150 20083 0.026666667 0.0111111111 0.38634921
## 40 42101009300 377 4609 21503 0.002603602 0.0000000000 0.40203949
## 41 42101009400 285 3871 17743 0.009299923 0.0000000000 0.42934642
## 42 42101009500 368 3588 21058 0.000000000 0.0027870680 0.38099220
## 43 42101009600 404 4504 23171 0.017984014 0.0000000000 0.37655417
## 44 42101010000 421 4256 31415 0.336701128 0.0063439850 0.37946429
## 45 42101010200 331 3240 20634 0.008950617 0.0024691358 0.41172840
## 46 42101010400 374 3498 21772 0.010863350 0.0022870212 0.36678102
## 47 42101012600 916 801 31838 0.485642946 0.0399500624 0.40823970
## 48 42101012700 223 689 7500 0.076923077 0.0000000000 0.33671988
## 49 42101012800 841 231 39643 0.831168831 0.0303030303 0.64935065
## 50 42101012900 586 526 41000 0.922053232 0.0000000000 0.54372624
## 51 42101013000 573 1003 35625 0.417746760 0.0368893320 0.50149551
## 52 42101013200 177 2190 14375 0.051598174 0.0000000000 0.31735160
## 53 42101013300 492 2217 29352 0.274695535 0.0211998196 0.46098331
## 54 42101014000 370 2855 13458 0.042732049 0.0122591944 0.41015762
## 55 42101014100 210 2366 12165 0.094674556 0.0000000000 0.41758242
## 56 42101014200 565 2091 30862 0.608321377 0.0162601626 0.45911047
## 57 42101014400 323 3217 23720 0.393534349 0.0027976376 0.35685421
## 58 42101014600 384 3231 26295 0.194986072 0.0000000000 0.30238316
## 59 42101014700 306 2449 16411 0.008166599 0.0000000000 0.31073908
## 60 42101015300 353 3382 14826 0.068598462 0.0165582496 0.34920166
## 61 42101015400 489 2316 31923 0.410621762 0.0000000000 0.07340242
## 62 42101015600 326 1989 14524 0.227249874 0.0045248869 0.29562594
## 63 42101015700 339 2759 22478 0.355201160 0.0050743023 0.32584270
## 64 42101015800 387 5854 31862 0.953877690 0.0015374103 0.38503587
## 65 42101016100 349 5435 19098 0.605887764 0.0000000000 0.32750690
## 66 42101016200 337 2442 13833 0.223177723 0.0000000000 0.32186732
## 67 42101016300 341 3776 15865 0.181938559 0.0052966102 0.28257415
## 68 42101016500 330 2722 16964 0.011756062 0.0022042616 0.33357825
## 69 42101016600 353 1630 14250 0.025766871 0.0055214724 0.39693252
## 70 42101016700 363 6947 16367 0.002303152 0.0061897222 0.37339859
## 71 42101017300 427 3126 21731 0.008637236 0.0000000000 0.36852207
## 72 42101017400 339 2667 15050 0.024371954 0.0029996250 0.34833146
## 73 42101017601 325 5863 11909 0.240661777 0.0010233669 0.30291660
## 74 42101017700 370 9159 15687 0.313789715 0.0005459111 0.28976963
## 75 42101017800 389 6340 14793 0.502996845 0.0037854890 0.33280757
## 76 42101018800 385 7257 22489 0.686371779 0.0000000000 0.34463277
## 77 42101018900 412 942 24032 0.580679406 0.0084925690 0.33757962
## 78 42101019000 427 6869 24650 0.591789198 0.0000000000 0.32581162
## 79 42101019200 390 7519 19375 0.408032983 0.0014629605 0.30110387
## 80 42101019700 422 7139 19877 0.186300602 0.0016809077 0.29079703
## 81 42101020000 378 1756 22786 0.185649203 0.0182232346 0.30410023
## 82 42101020100 349 7732 20436 0.018882566 0.0010346611 0.40597517
## 83 42101020300 371 3147 23277 0.032729584 0.0019065777 0.34413727
## 84 42101020400 386 3398 29814 0.016774573 0.0020600353 0.34873455
## 85 42101027500 472 4823 33953 0.226622434 0.0008293593 0.32821895
## 86 42101027600 463 3932 31316 0.033825025 0.0073753815 0.39267548
## 87 42101027800 426 4867 25690 0.034929114 0.0000000000 0.41956030
## 88 42101028000 485 4819 28281 0.021996265 0.0076779415 0.35692052
## 89 42101028100 401 4051 36129 0.057516663 0.0108615157 0.32189583
## 90 42101028200 457 5448 29142 0.137114537 0.0014684288 0.30231278
## 91 42101028300 425 7270 24725 0.048693260 0.0011004127 0.33301238
## 92 42101029300 375 2898 19205 0.652173913 0.0027605245 0.34817115
## 93 42101029400 386 3232 21288 0.368811881 0.0021658416 0.34096535
## 94 42101030000 388 7150 23876 0.322517483 0.0036363636 0.35356643
## 95 42101030100 427 6050 32224 0.505289256 0.0019834711 0.36082645
## 96 42101030200 411 6694 39072 0.695100090 0.0043322378 0.36241410
## 97 42101036600 1505 618 87027 0.953074434 0.0161812298 0.61650485
## year geometry
## 1 2000 MULTIPOLYGON (((2697812 -65...
## 2 2000 MULTIPOLYGON (((2696038 -65...
## 3 2000 MULTIPOLYGON (((2690929 -64...
## 4 2000 MULTIPOLYGON (((2693009 -66...
## 5 2000 MULTIPOLYGON (((2697206 -67...
## 6 2000 MULTIPOLYGON (((2696968 -68...
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## 22 2000 MULTIPOLYGON (((2692473 -76...
## 23 2000 MULTIPOLYGON (((2692147 -78...
## 24 2000 MULTIPOLYGON (((2694362 -76...
## 25 2000 MULTIPOLYGON (((2694135 -78...
## 26 2000 MULTIPOLYGON (((2693880 -80...
## 27 2000 MULTIPOLYGON (((2690184 -81...
## 28 2000 MULTIPOLYGON (((2693677 -82...
## 29 2000 MULTIPOLYGON (((2670305 -64...
## 30 2000 MULTIPOLYGON (((2672050 -64...
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## 33 2000 MULTIPOLYGON (((2678189 -65...
## 34 2000 MULTIPOLYGON (((2682357 -65...
## 35 2000 MULTIPOLYGON (((2682978 -65...
## 36 2000 MULTIPOLYGON (((2685201 -66...
## 37 2000 MULTIPOLYGON (((2687003 -63...
## 38 2000 MULTIPOLYGON (((2685048 -63...
## 39 2000 MULTIPOLYGON (((2680537 -64...
## 40 2000 MULTIPOLYGON (((2677296 -63...
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## 42 2000 MULTIPOLYGON (((2673363 -61...
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## 44 2000 MULTIPOLYGON (((2669883 -59...
## 45 2000 MULTIPOLYGON (((2675291 -61...
## 46 2000 MULTIPOLYGON (((2678548 -61...
## 47 2000 MULTIPOLYGON (((2696344 -63...
## 48 2000 MULTIPOLYGON (((2698113 -63...
## 49 2000 MULTIPOLYGON (((2699439 -63...
## 50 2000 MULTIPOLYGON (((2700853 -63...
## 51 2000 MULTIPOLYGON (((2699624 -62...
## 52 2000 MULTIPOLYGON (((2696642 -61...
## 53 2000 MULTIPOLYGON (((2694718 -60...
## 54 2000 MULTIPOLYGON (((2694993 -58...
## 55 2000 MULTIPOLYGON (((2695544 -59...
## 56 2000 MULTIPOLYGON (((2701823 -61...
## 57 2000 MULTIPOLYGON (((2700270 -58...
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## 59 2000 MULTIPOLYGON (((2695197 -57...
## 60 2000 MULTIPOLYGON (((2695468 -54...
## 61 2000 MULTIPOLYGON (((2696989 -55...
## 62 2000 MULTIPOLYGON (((2700636 -56...
## 63 2000 MULTIPOLYGON (((2702130 -56...
## 64 2000 MULTIPOLYGON (((2702836 -57...
## 65 2000 MULTIPOLYGON (((2702305 -54...
## 66 2000 MULTIPOLYGON (((2700153 -54...
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## 72 2000 MULTIPOLYGON (((2697775 -52...
## 73 2000 MULTIPOLYGON (((2699943 -52...
## 74 2000 MULTIPOLYGON (((2704118 -49...
## 75 2000 MULTIPOLYGON (((2704108 -53...
## 76 2000 MULTIPOLYGON (((2709922 -48...
## 77 2000 MULTIPOLYGON (((2710580 -48...
## 78 2000 MULTIPOLYGON (((2711724 -43...
## 79 2000 MULTIPOLYGON (((2707100 -48...
## 80 2000 MULTIPOLYGON (((2699239 -42...
## 81 2000 MULTIPOLYGON (((2696092 -50...
## 82 2000 MULTIPOLYGON (((2695815 -44...
## 83 2000 MULTIPOLYGON (((2697659 -42...
## 84 2000 MULTIPOLYGON (((2697239 -41...
## 85 2000 MULTIPOLYGON (((2702629 -32...
## 86 2000 MULTIPOLYGON (((2699591 -32...
## 87 2000 MULTIPOLYGON (((2697819 -32...
## 88 2000 MULTIPOLYGON (((2696067 -38...
## 89 2000 MULTIPOLYGON (((2697957 -36...
## 90 2000 MULTIPOLYGON (((2697957 -36...
## 91 2000 MULTIPOLYGON (((2699709 -38...
## 92 2000 MULTIPOLYGON (((2711963 -42...
## 93 2000 MULTIPOLYGON (((2713128 -47...
## 94 2000 MULTIPOLYGON (((2716617 -41...
## 95 2000 MULTIPOLYGON (((2713405 -39...
## 96 2000 MULTIPOLYGON (((2714957 -37...
## 97 2000 MULTIPOLYGON (((2700290 -66...print(selectCentroids)## Simple feature collection with 97 features and 2 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: 2667829 ymin: -84785.82 xmax: 2719496 ymax: -32044.96
## Projected CRS: NAD_1983_StatePlane_Pennsylvania_North_FIPS_3701_Feet
## First 10 features:
## TotalPop geometry Selection_Type
## 1 2576 MULTIPOLYGON (((2697812 -65... Select by Centroids
## 2 1355 MULTIPOLYGON (((2696038 -65... Select by Centroids
## 3 2577 MULTIPOLYGON (((2690929 -64... Select by Centroids
## 4 4387 MULTIPOLYGON (((2693009 -66... Select by Centroids
## 5 1071 MULTIPOLYGON (((2697206 -67... Select by Centroids
## 6 1384 MULTIPOLYGON (((2696968 -68... Select by Centroids
## 7 2550 MULTIPOLYGON (((2693753 -67... Select by Centroids
## 8 8461 MULTIPOLYGON (((2693685 -68... Select by Centroids
## 9 4969 MULTIPOLYGON (((2696756 -68... Select by Centroids
## 10 5808 MULTIPOLYGON (((2699725 -68... Select by Centroidsnow the textbook challenges us to create a side by side maps of the various selection types using the facet_wrap layer tool from earlier
all_types_tracts00 <-
rbind(clip,selection,selectCentroids)imma set the size of the output. wasnt happy with the default
ggplot() + #creates the plot
geom_sf(data=st_union(tracts00)) + #creates our city outline
geom_sf(data = all_types_tracts00, aes(fill = q5(TotalPop))) +
scale_fill_manual(values = palette5,
labels = qBr(all_types_tracts00, "TotalPop"),
name = "Popluation\n(Quintile Breaks)") +
labs(title = "Total Population", subtitle = "Philadelphia; 2000") +
facet_wrap(~Selection_Type) +
mapTheme()
the below code redoes our centroid approach and labels those tracts
whose centroid falls into the buffer as transit oriented development. It
label those tracts that do not fall into the buffer as non TOD.
allTracts.group <-
rbind( #since left join takes the first argument as the total number of rows, we need to bind the two subsets of the full datset on to of each other.
st_centroid(allTracts)[buffer,] %>% #selects the tracts whose centroids fall in the buffer
st_drop_geometry() %>% # drops the geometry to allow easier manipulation as a dataframe or maybe you just cant have two geometries per sf
left_join(allTracts) %>% #joins the missing data in our centroid tract data to the data from allTracts. this command outomatically matches columns to prevent duplication
st_sf() %>% #converts that data to an sf
mutate(TOD = "TOD"), #creates the TOD column and labels all the current data as TOD, not the comma. everything this line and above is the first argument of rbind
st_centroid(allTracts)[buffer, op = st_disjoint] %>% # the disjoint option basically inverses the command so that we select all tracts that are not in buffer
st_drop_geometry() %>%
left_join(allTracts) %>%
st_sf() %>%
mutate(TOD = "Non-TOD")) %>% #labels every tract not in the buffer as non TOD
mutate(MedRent.inf = ifelse(year == "2000", MedRent * 1.42, MedRent)) # multiplies all the year 2000 rents by inflation## Warning: st_centroid assumes attributes are constant over geometries## Joining with `by = join_by(GEOID, MedRent, TotalPop, MedHHInc, pctWhite,
## pctBachelors, pctPoverty, year)`## Warning: st_centroid assumes attributes are constant over geometries## Joining with `by = join_by(GEOID, MedRent, TotalPop, MedHHInc, pctWhite,
## pctBachelors, pctPoverty, year)`now the textbook is giving us an excercise to recreate a map
ggplot() + #creates the plot
geom_sf(data=st_union(allTracts.group)) + #creates our city outline
geom_sf(data = allTracts.group, aes(fill = q5(MedRent.inf)), colour = NA) + # creates of visualization of the rents by tract
scale_fill_manual(values = palette5, # choosing fill colors and other aesthetic attributes
labels = qBr(allTracts.group, "MedRent"),
name = "Rent\n(Quintile Breaks)") +
geom_sf(data = buffer, fill = "grey", color = "red", alpha = 0.3) + # creates the buffer layer
labs(title = "Median Rent", subtitle = "Real dollars: red borders denote buffer areas around subway stops") +
facet_wrap(~year) +
mapTheme()
now that we have created our visual aid, we wanna do some comparisons in
other formats including tables
allTracts.Summary <-
st_drop_geometry(allTracts.group) %>%
group_by(year, TOD) %>%
summarize(Rent = mean(MedRent, na.rm = T), #the summarize function creates one row for each combination of grouping variable
Population = mean(TotalPop, na.rm = T), # we are manually specifiyinging our rows and calculations to be done in each row
Percent_White = mean(pctWhite, na.rm = T),
Percent_Bach = mean(pctBachelors, na.rm = T),
Percent_Poverty = mean(pctPoverty, na.rm = T))## `summarise()` has grouped output by 'year'. You can override using the
## `.groups` argument.kable(allTracts.Summary) %>%
kable_styling() %>%
footnote(general_title = "\n",
general = "Table 1.2")| year | TOD | Rent | Population | Percent_White | Percent_Bach | Percent_Poverty |
|---|---|---|---|---|---|---|
| 2000 | Non-TOD | 470.5458 | 3966.789 | 0.4695256 | 0.0096146 | 0.3735100 |
| 2000 | TOD | 469.8247 | 4030.742 | 0.3848745 | 0.0161826 | 0.4031254 |
| 2017 | Non-TOD | 821.1642 | 4073.547 | 0.4396967 | 0.0116228 | 0.2373258 |
| 2017 | TOD | 913.3750 | 3658.500 | 0.4803197 | 0.0288166 | 0.3080936 |
|
|
||||||
| Table 1.2 |
now we are gonna take our data frame and inverse it to see the variables as the rows
allTracts.Summary %>%
unite(year.TOD, year, TOD, sep = ": ", remove = T) %>% # creates a new column that unites year and TOD. Remove option removes the old columns I imagine
print() %>%
gather(Variable, Value, -year.TOD) %>% # converts our data to long format. year.TOD is our "grouping variable", "variable" is the key, and "value" is the value. in practice when using the gather function, the grouping variable column is the column that stays put in the dataframe. It's the variable that the other variables get organized around. the key variable is what we call the new column that is made up of a stack of all the other variable that are not the grouping variable. the - sign denotes that we are excluding a variable from being gathered.
print() %>%
mutate(Value = round(Value, 2)) %>%
spread(year.TOD, Value) %>% # this inverts our table, spreading year.TOD into separate columns alongside a Variable column that now has unique values for each row. Value is specified as the column to appear as values in the matrix
kable() %>%
kable_styling() %>%
footnote(general_title = "\n",
general = "Table 1.3")## # A tibble: 4 × 6
## year.TOD Rent Population Percent_White Percent_Bach Percent_Poverty
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2000: Non-TOD 471. 3967. 0.470 0.00961 0.374
## 2 2000: TOD 470. 4031. 0.385 0.0162 0.403
## 3 2017: Non-TOD 821. 4074. 0.440 0.0116 0.237
## 4 2017: TOD 913. 3658. 0.480 0.0288 0.308
## # A tibble: 20 × 3
## year.TOD Variable Value
## <chr> <chr> <dbl>
## 1 2000: Non-TOD Rent 471.
## 2 2000: TOD Rent 470.
## 3 2017: Non-TOD Rent 821.
## 4 2017: TOD Rent 913.
## 5 2000: Non-TOD Population 3967.
## 6 2000: TOD Population 4031.
## 7 2017: Non-TOD Population 4074.
## 8 2017: TOD Population 3658.
## 9 2000: Non-TOD Percent_White 0.470
## 10 2000: TOD Percent_White 0.385
## 11 2017: Non-TOD Percent_White 0.440
## 12 2017: TOD Percent_White 0.480
## 13 2000: Non-TOD Percent_Bach 0.00961
## 14 2000: TOD Percent_Bach 0.0162
## 15 2017: Non-TOD Percent_Bach 0.0116
## 16 2017: TOD Percent_Bach 0.0288
## 17 2000: Non-TOD Percent_Poverty 0.374
## 18 2000: TOD Percent_Poverty 0.403
## 19 2017: Non-TOD Percent_Poverty 0.237
## 20 2017: TOD Percent_Poverty 0.308| Variable | 2000: Non-TOD | 2000: TOD | 2017: Non-TOD | 2017: TOD |
|---|---|---|---|---|
| Percent_Bach | 0.01 | 0.02 | 0.01 | 0.03 |
| Percent_Poverty | 0.37 | 0.40 | 0.24 | 0.31 |
| Percent_White | 0.47 | 0.38 | 0.44 | 0.48 |
| Population | 3966.79 | 4030.74 | 4073.55 | 3658.50 |
| Rent | 470.55 | 469.82 | 821.16 | 913.38 |
|
|
||||
| Table 1.3 |
now the textbook shows us how to display this data as bar plots
allTracts.Summary %>%
gather(Variable, Value, -year, -TOD) %>% # gather our data leaving both year and TOD as grouping variables
ggplot(aes(year, Value, fill = TOD)) + # graph year on the x axis and value on the y axis, and specifies that we are filling the bars based on the two TOD groups
geom_bar(stat = "identity", position = "dodge") +
facet_wrap(~Variable, scales = "free", ncol=5) + #specifies that we want separare plots for each of the variables
scale_fill_manual(values = c("#bae4bc", "#0868ac")) +
labs(title = "Indicator differences across time and space") +
plotTheme() + theme(legend.position="bottom",
strip.text.x = element_text(size = 10)) # changes the size of the strip labels at the top of each plot.## Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
now the textbook creates 3 submarkets. One for the center of the city
and one for each subway line. we want to see if the central city has had
a disproportionate increase in rental prices compared to the other
transit oriented development areas. if so, we may conclude that the
increase at the center of the city skewed or TOD data.
centerCity <-
st_intersection( #creates a layer that only includes the intersecting buffers of the two subway lines
st_buffer(filter(septaStops, Line == "El"), 2640) %>% st_union(), #we start from our union of points sf layer. so we first create a buffer for both separate lines
st_buffer(filter(septaStops, Line == "Broad_St"), 2640) %>% st_union()) %>%
st_sf() %>% #converts to sf layer. not sure why this is necessary since we never dropped geometry
mutate(Submarket = "Center City") #creates a column in our sf called submarket that is labelled City Center.
el <-
st_buffer(filter(septaStops, Line == "El"), 2640) %>% st_union() %>%
st_sf() %>%
st_difference(centerCity) %>% #erases any portion of the layer that overlaps with the city center
mutate(Submarket = "El")## Warning: attribute variables are assumed to be spatially constant throughout
## all geometriesbroad.st <-
st_buffer(filter(septaStops, Line == "Broad_St"), 2640) %>% st_union() %>%
st_sf() %>%
st_difference(centerCity) %>%
mutate(Submarket = "Broad Street")## Warning: attribute variables are assumed to be spatially constant throughout
## all geometriesthreeMarkets <- rbind(el, broad.st, centerCity) #stacks the three separate sfs on top of each othernow we are going to assign all our tracts to three 4 categories (central city, el, non tod.. etc) based on what buffer each centroid falls in.
allTracts.threeMarkets <-
st_join(st_centroid(allTracts), threeMarkets) %>% # st_join joins two separate sf's together. in this case it joins threemarkets to the left of alltracts based on which market buffer from threemarkets each centroid from alltracts falls into. the geomtry that is kept is the centroid from alltracts
st_drop_geometry() %>% # drops the centroid geometry
left_join(allTracts) %>% #joins alltracts to the left of what we are left with from the above code matching columns automatically. essentially, we are just getting the geometries from allTracts
mutate(Submarket = replace_na(Submarket, "Non-TOD")) %>% #replaces all the na's in the submarket column with "Non-TOD"
st_sf() ## Warning: st_centroid assumes attributes are constant over geometries## Joining with `by = join_by(GEOID, MedRent, TotalPop, MedHHInc, pctWhite,
## pctBachelors, pctPoverty, year)`It doesnt give us the code to graph the separate markets so let’s try to do it oursevles
ggplot() + #creates the plot
geom_sf(data=st_union(allTracts.threeMarkets)) + #creates our city outline
geom_sf(data = allTracts.threeMarkets, aes(fill = Submarket)) +
mapTheme()